MLA-C01

Explanation:
Correct option:
Apply Extreme Gradient Boosting (XGBoost) for its ability to handle imbalanced datasets effectively through regularization, weighted classes, and optimized computational efficiency
The XGBoost (eXtreme Gradient Boosting) is a popular and efficient open-source implementation of the gradient boosted trees algorithm. Gradient boosting is a supervised learning algorithm that tries to accurately predict a target variable by combining multiple estimates from a set of simpler models. The XGBoost algorithm performs well in machine learning competitions for the following reasons:
Its robust handling of a variety of data types, relationships, distributions.
The variety of hyperparameters that you can fine-tune.
XGBoost is an extension of Gradient Boosting that includes additional features such as regularization, handling of missing values, and support for weighted classes, making it particularly well-suited for imbalanced datasets like fraud detection. It also offers significant computational efficiency, which is beneficial when working with large datasets.
via - https://aws.amazon.com/what-is/boosting/
Incorrect options:
Use Adaptive Boosting (AdaBoost) to focus on correcting the errors of weak classifiers, giving more weight to incorrectly classified instances during each iteration - AdaBoost works by focusing on correcting the errors of weak classifiers, assigning more weight to misclassified instances in each iteration. However, it may struggle with noisy data and extreme class imbalance, as it can overemphasize hard-to-classify instances.
Implement Gradient Boosting to sequentially train weak learners, using the gradient of the loss function to improve performance on the minority class - Gradient Boosting is a powerful technique that uses the gradient of the loss function to improve the model iteratively. While it can be adapted to handle class imbalance, it does not inherently provide the same level of flexibility and computational optimization as XGBoost for this specific problem.
Use Gradient Boosting and manually adjust the learning rate and class weights to improve performance on the minority class, avoiding the complexities of XGBoost - While manually adjusting the learning rate and class weights in Gradient Boosting can help, XGBoost already provides built-in mechanisms to handle these challenges more effectively, including advanced regularization techniques and hyperparameter optimization.
References:
https://aws.amazon.com/what-is/boosting/
https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html
https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html
https://aws.amazon.com/blogs/gametech/fraud-detection-for-games-using-machine-learning/
https://d1.awsstatic.com/events/reinvent/2019/REPEAT_1_Build_a_fraud_detection_system_with_Amazon_SageMaker_AIM359-R1.pdf

Explanation:
Correct option:
Factorization Machines Algorithm to model user-item interactions for collaborative filtering
The Factorization Machines algorithm is a general-purpose supervised learning algorithm that you can use for both classification and regression tasks. It is an extension of a linear model that is designed to capture interactions between features within high dimensional sparse datasets economically. For example, in a click prediction system, the Factorization Machines model can capture click rate patterns observed when ads from a certain ad-category are placed on pages from a certain page-category. Factorization machines are a good choice for tasks dealing with high dimensional sparse datasets, such as click prediction and item recommendation.
Factorization Machines is well-suited for collaborative filtering. It excels at modeling sparse user-item interactions, making it ideal for large-scale recommendation systems where there are many users and items but relatively few interactions for each user-item pair. This algorithm can effectively capture latent factors to provide personalized recommendations.
Mapping use cases to built-in algorithms:
via - https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
Incorrect options:
XGBoost Algorithm to rank the products based on user behavior and demographic features - XGBoost is a powerful algorithm for ranking and classification tasks, but it’s not optimized for collaborative filtering, which is crucial for personalized recommendations in this context.
BlazingText Algorithm to analyze the text in user reviews and identify product similarities - BlazingText is effective for text classification and word embedding but is not specifically designed for recommendation systems. While it can be used to analyze user reviews, it does not address the core requirement of user-item interaction modeling.
K-Means Algorithm to cluster users into segments and recommend products based on these segments - K-Means is useful for clustering users into segments, but this approach is more generalized and does not provide the level of personalization required for individual recommendations based on specific user-item interactions.
References:
https://docs.aws.amazon.com/sagemaker/latest/dg/fact-machines.html
https://aws.amazon.com/blogs/machine-learning/accelerate-and-improve-recommender-system-training-and-predictions-using-amazon-sagemaker-feature-store/
https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

Explanation:
Correct option:
Set up a continuous monitoring job with SageMaker Clarify to track changes in feature distribution over time and alert you when a significant feature attribution drift is detected, allowing you to investigate and potentially retrain the model
A drift in the distribution of live data for models in production can result in a corresponding drift in the feature attribution values, just as it could cause a drift in bias when monitoring bias metrics. Amazon SageMaker Clarify feature attribution monitoring helps data scientists and ML engineers monitor predictions for feature attribution drift on a regular basis.
Continuous monitoring with SageMaker Clarify is the most effective approach for detecting changes in data distribution. By tracking feature distributions over time, you can identify when a significant shift occurs, investigate its impact on model performance, and decide if retraining is necessary. This proactive approach helps ensure that your model remains accurate and fair as the underlying data evolves.
via -
https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-feature-attribution-drift.html
Incorrect options:
Use SageMaker Clarify’s bias detection capabilities to analyze the model’s output and identify any disparities between different demographic groups, retraining the model only if significant bias is detected
- While SageMaker Clarify’s bias detection is useful, focusing solely on bias in the model’s output doesn’t address the broader issue of shifts in feature distribution that can impact overall model performance. Continuous monitoring is needed to detect such changes proactively.
Implement a random sampling process to manually review a subset of incoming data each month, comparing it with the original training data to check for distribution changes - Manual reviews of data can be labor-intensive, error-prone, and may not catch distribution changes in a timely manner. Automated monitoring with SageMaker Clarify is more efficient and reliable.
Use SageMaker Clarify to perform a one-time bias analysis during model training, ensuring that the model is initially fair and accurate, and manually monitor future data distribution changes - A one-time bias analysis during training helps ensure initial fairness, but it doesn’t address ongoing changes in data distribution after the model is deployed. Continuous monitoring is necessary to maintain model performance over time.
Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-monitor-feature-attribution-drift.html

Explanation:
Correct option:
Use Amazon SageMaker Data Wrangler within Amazon SageMaker Canvas
Use Amazon SageMaker Data Wrangler in Amazon SageMaker Canvas to prepare, featurize and analyze your data. You can integrate a Data Wrangler data preparation flow into your machine learning (ML) workflows to simplify and streamline data pre-processing and feature engineering using little to no coding. You can also add your own Python scripts and transformations to customize workflows.
Data Flow C Create a data flow to define a series of ML data prep steps. You can use a flow to combine datasets from different data sources, identify the number and types of transformations you want to apply to datasets, and define a data prep workflow that can be integrated into an ML pipeline.
Transform C Clean and transform your dataset using standard transforms like string, vector, and numeric data formatting tools. Featurize your data using transforms like text and date/time embedding and categorical encoding.
Generate Data Insights C Automatically verify data quality and detect abnormalities in your data with Data Wrangler Data Quality and Insights Report.
Analyze C Analyze features in your dataset at any point in your flow. Data Wrangler includes built-in data visualization tools like scatter plots and histograms, as well as data analysis tools like target leakage analysis and quick modeling to understand feature correlation.
Export C Export your data preparation workflow to a different location.
SageMaker Canvas features for ML lifecycle:
via - https://aws.amazon.com/sagemaker/canvas/
Incorrect options:
Prepare data using Amazon EMR in Studio - The integration between Amazon EMR and Amazon SageMaker Studio provides a scalable environment for large-scale data preparation for machine learning. This option is viable only if users are comfortable writing code.
Use Amazon SageMaker Data Wrangler within Amazon SageMaker JumpStart - Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can evaluate, compare, and select FMs quickly based on pre-defined quality and responsibility metrics to perform tasks like article summarization and image generation. SageMaker JumpStart is not the right tool for the given use case.
Prepare data with SQL in Amazon SageMaker Studio. The SQL extension connections to Amazon Athena for working with datasets - Amazon SageMaker Studio provides a built-in SQL extension. This
extension allows data scientists to perform tasks such as sampling, exploratory analysis, and feature engineering directly within the JupyterLab notebooks. However, this option does not offer any no-code features.
References:
https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-data-prep.html
https://docs.aws.amazon.com/sagemaker/latest/dg/data-prep.html

Explanation:
Correct option:
Amazon Bedrock focuses on providing a managed service for deploying pre-trained foundation models from various providers, whereas Amazon SageMaker JumpStart offers a range of pre-built solutions, including models, notebooks, and algorithms for both machine learning and generative AI use cases Amazon Bedrock is the easiest way to build and scale generative AI applications with foundation models. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.
Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can evaluate, compare, and select FMs quickly based on pre-defined quality and responsibility metrics to perform tasks like article summarization and image generation. SageMaker JumpStart provides managed infrastructure and tools to accelerate scalable, reliable, and secure model building, training, and deployment of ML models.
This option correctly summarizes the distinction between the two services. Amazon Bedrock is designed for deploying pre-trained foundation models (such as those from AI21 Labs, Anthropic, and Stability AI) and is optimized for generative AI tasks. Amazon SageMaker JumpStart, in contrast, provides a comprehensive set of pre-built solutions that include machine learning models, algorithms, and notebooks, making it versatile for both traditional ML and generative AI.
Incorrect options:
Amazon Bedrock is designed specifically for building and deploying custom machine learning models, while Amazon SageMaker JumpStart is tailored for deploying pre-trained large language models (LLMs) with minimal customization - This option is incorrect as it states that Amazon Bedrock is for building and deploying custom machine learning models. Bedrock is actually focused on deploying and scaling pre-trained foundation models, particularly for generative AI tasks. SageMaker JumpStart, on the other hand, offers pre-built solutions for a variety of use cases, not just LLMs.
Amazon Bedrock is ideal for quick deployment of computer vision models, while Amazon SageMaker JumpStart specializes in deploying natural language processing models - This option oversimplifies the services by suggesting they specialize in different.
Amazon Bedrock provides a simplified interface for training and tuning models from scratch, while Amazon SageMaker JumpStart is primarily for deploying third-party models with limited customization - This option is incorrect as it positions Amazon Bedrock as a service for training models from scratch, which is not its primary focus. Bedrock is about deploying pre-trained foundation models, while SageMaker JumpStart provides broader ML solutions, including both pre-trained models and full-featured templates for various use cases.
References:
https://aws.amazon.com/bedrock/
https://aws.amazon.com/sagemaker/jumpstart/
https://aws.amazon.com/what-is/generative-ai/